Positive Effect of Cognitive Reserve on Episodic Memory, Executive and Attentional Functions Taking Into Account Amyloid-Beta, Tau, and Apolipoprotein E Status

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Positive Effect of Cognitive Reserve on Episodic Memory, Executive and Attentional Functions Taking Into Account Amyloid-Beta, Tau, and Apolipoprotein E Status

Justinas Narbutas^1,2, Daphne Chylinski¹, Maxime Van Egroo¹, Mohamed Ali Bahri¹, Ekaterina Koshmanova¹, Gabriel Besson¹, Vincenzo Muto¹, Christina Schmidt^1,2, André Luxen¹, Evelyne Balteau¹, Christophe Phillips¹, Pierre Maquet^1,3, Eric Salmon^1,2,3, Gilles Vandewalle¹, Christine Bastin^1,2 and Fabienne Collette^1,2*

• ¹GIGA Institute, Cyclotron Research Centre In Vivo Imaging, University of Liège, Liège, Belgium
• ²Psychology and Neuroscience of Cognition Research Unit, Faculty of Psychology and Educational Sciences, University of Liège, Liège, Belgium
• ³Department of Neurology, CHU de Liège, Liège, Belgium

Studies exploring the simultaneous influence of several physiological and environmental factors on domain-specific cognition in late middle-age remain scarce. Therefore, our objective was to determine the respective contribution of modifiable risk/protective factors (cognitive reserve and allostatic load) on specific cognitive domains (episodic memory, executive functions, and attention), taking into account non-modifiable factors [sex, age, and genetic risk for Alzheimer’s disease (AD)] and AD-related biomarker amount (amyloid-beta and tau/neuroinflammation) in a healthy late-middle-aged population. One hundred and one healthy participants (59.4 ± 5 years; 68 women) were evaluated for episodic memory, executive and attentional functioning via neuropsychological test battery. Cognitive reserve was determined by the National Adult Reading Test. The allostatic load consisted of measures of lipid metabolism and sympathetic nervous system functioning. The amyloid-beta level was assessed using positron emission tomography in all participants, whereas tau/neuroinflammation positron emission tomography scans and apolipoprotein E genotype were available for 58 participants. Higher cognitive reserve was the main correlate of better cognitive performance across all domains. Moreover, age was negatively associated with attentional functioning, whereas sex was a significant predictor for episodic memory, with women having better performance than men. Finally, our results did not show clear significant associations between performance over any cognitive domain and apolipoprotein E genotype and AD biomarkers. This suggests that domain-specific cognition in late healthy midlife is mainly determined by a combination of modifiable(Which suggests that someone out there knows how to create this reserve.)(cognitive reserve) and non-modifiable factors (sex and age) rather than by AD biomarkers and genetic risk for AD.

Introduction

The constant increase in life expectancy is associated with an increase in age-related physical and cognitive dysfunctions (WHO, 2011). Age-related cognitive changes include a broad spectrum of clinical categories, including cognitively normal aging, subjective cognitive decline, and mild cognitive impairment, and dementia stages. It is now clear that age-related brain changes associated with Alzheimer’s disease (AD) begin at least as early as in late middle age (Beason-Held et al., 2013; Jack and Holtzman, 2013; Ritchie et al., 2015; Coupé et al., 2019). For example, deposits of amyloid-beta (Aβ) and tau protein, two main biomarkers of AD pathophysiology, are observed decades before the first clinical signs of cognitive decline (Jack and Holtzman, 2013).

The presence of these biomarkers does not, however, mean that people will necessarily develop clinical AD; they are only indicative of an increased risk for developing the disease (Dumurgier et al., 2017; Roe et al., 2018). This implies that the presence of risk factors can potentially be counterbalanced by protective factors, which may explain the diversity in decline rates and trajectories of cognitive aging (Nyberg et al., 2012; Reuter-Lorenz and Park, 2014; Cabeza et al., 2018). Besides Aβ and tau proteins (Betthauser et al., 2019), several physiological and psychological environmental protective and risk factors were proposed to explain this variability (Norton et al., 2014; Livingston et al., 2017), such as cognitive reserve (Cabeza et al., 2018; Stern et al., 2018), allostatic load (Karlamangla et al., 2014), and genetics, particularly with ε4 polymorphism in apolipoprotein E (APOE) gene (Greenwood et al., 2014).

As current research in cognitive aging shifts to primary and secondary prevention, it is important to identify predictors of cognitive performance as early as possible. Late midlife is, therefore, an interesting time window, sometimes characterized by first manifestations of AD pathophysiology and subtle cognitive changes or cognitive complaints. Moreover, several chronic diseases or lifestyle choices such as hypertension, diabetes, obesity, sedentary, and smoking habits are related to subsequent brain pathology and cognitive decline in later life (Debette et al., 2011; Livingston et al., 2020). In addition, the factors affecting cognition in late midlife differ from factors that are important in older age (McFall et al., 2019). Therefore, a better understanding of risk and protective factors for cognition in late midlife would provide valuable insight for lifestyle changes and/or interventions.

Cognitive reserve refers to adaptability (i.e., efficiency, capacity, and flexibility) of cognitive processes that help counteract physiological changes associated with brain aging, pathology, or insult (Stern et al., 2018). Cognitive reserve helps to maintain normal cognitive performance in the presence of pathology through the recruitment of alternative brain networks, altered brain metabolism (Bastin et al., 2012), or alternative cognitive strategies. The influence of cognitive reserve factors on cognition in normal aging observed in cross-sectional studies is grounded on the positive association between cognitive efficiency, which refers to optimal performance on a cognitive task (Hoffman and Schraw, 2010), and (a) higher level of education and intelligence (Fratiglioni and Wang, 2007; Meng and D’Arcy, 2012; Osone et al., 2015; Bright et al., 2018) for reviews), (b) employment complexity and autonomy (Bosma et al., 2003; Ansiau et al., 2005; Baldivia et al., 2008; Then et al., 2014 for reviews), and (c) physical activity, engagement in cognitively demanding leisure activities, and/or sustained social interactions (Wang et al., 2012 for a review). These factors also seem to influence cognitive decline in longitudinal studies (Schooler et al., 1999; Bosma et al., 2003; Manly et al., 2003; Andel et al., 2014; Hughes et al., 2018), but the effects are more mixed than in cross-sectional designs (see Pettigrew and Soldan, 2019 for a review and discussion). They also delay pathological cognitive decline, with a later onset of AD in individuals with higher reserve (Stern et al., 2018). Importantly, these cognitive reserve factors may have compensatory effects already in middle-aged people, as shown for global cognition and visual abilities (Ferreira et al., 2017) and other cognitive functions, including episodic memory, executive and attentional processes, but excluding processing speed (De Frias and Dixon, 2014; Hughes et al., 2018).

In contrast, the strains put on physiology and health have a negative influence on cognitive aging. Several studies emphasized a link between cardiovascular functioning and cognition already in midlife (Dahle et al., 2009; Gupta et al., 2015; Zeki Al Hazzouri et al., 2017), whereas lipid and glucose metabolism, inflammation, cortisol level, and sympathetic nervous system functioning were associated with early cognitive decline (Karlamangla et al., 2005; Wright et al., 2006; Dahle et al., 2009; Ownby, 2010; Sartori et al., 2012; Ma et al., 2017; Narbutas et al., 2019). The allostatic load was proposed as a comprehensive index gathering the physiological stressors (Karlamangla et al., 2002) that are negatively associated with episodic memory performance and executive functioning in middle-aged and older adults (Karlamangla et al., 2014).

Also, ε4 polymorphism in the APOE gene is an established genetic risk factor for late-onset AD (Corder et al., 1993). However, its link with cognitive performance in healthy late middle-aged individuals remains inconsistent in cross-sectional studies, with positive, negative, and null associations reported with cognition, especially for memory performance (for a review, see Salvato, 2015).

Moreover, there is a broad discussion about the implication of both pathologic AD proteins (Aβ and tau) in cognitive performance in late midlife. Whereas some studies found a link between positron emission tomography (PET) Aβ accumulation and episodic memory decline in midlife (Hedden et al., 2013; Schultz et al., 2015; Clark et al., 2016, 2019; Farrell et al., 2017), other studies did not find any significant relationship (Johnson et al., 2014; Oh et al., 2014; Song et al., 2015; Mielke et al., 2016). Furthermore, greater PET Aβ deposition in middle age was associated with lower vocabulary level (Farrell et al., 2017) and decreased attentional and executive functioning in some (Doherty et al., 2015; Clark et al., 2016; Mielke et al., 2016) but not all studies (Johnson et al., 2014; Hollands et al., 2015; Lim et al., 2015; Pietrzak et al., 2015; Song et al., 2015). A recent study combining both Aβ and second-generation tau PET tracers in a group of late-middle-aged healthy participants demonstrated a combined detrimental influence on cognition of both proteins: participants with both elevated Aβ and tau levels experienced three times faster cognitive decline in comparison with those having just one or no elevated biomarkers (Betthauser et al., 2019). In addition, each biomarker may be associated with specific aspects of cognition, with tau level associated with verbal episodic memory and Aβ deposit with executive performance (Terrera et al., 2020).

Studies exploring the simultaneous influence of several physiological and psychological risk and protective factors on cognition in late middle age remain scarce. Our previous cross-sectional study (Narbutas et al., 2019) in late middle-aged individuals demonstrated that several categories of modifiable lifestyle factors, i.e., cognitive reserve and allostatic load, and particularly some subfactors, i.e., crystallized intelligence, sympathetic nervous system functioning, and lipid metabolism, may explain the variability in cognitive performance measured on a global composite score, which is sensitive to early cognitive change (Papp et al., 2017). In a similar vein, in a sample of healthy older adults aged from 53 to 85 years old, stable memory performance was associated with higher education, lower depressive symptoms, better living status, normal body mass index (BMI), normal heart rate, and more social activities, whereas declining memory performance was associated with fewer novel cognitive activities, reduced grip strength, abnormal heart rate, and poorer gait (McFall et al., 2019). Furthermore, McFall et al. (2019) reported that the association of these factors with memory status was more marked in the young-old group (mean age: 64.1 years).

In the continuity of these studies, our objective was to determine the respective contribution of modifiable risk/protective factors (allostatic load and cognitive reserve) on specific cognitive domains (episodic memory, executive functions, and attention), taking into account non-modifiable factors (sex, age, and APOE status) and brain AD-related biomarker level (Aβ and tau) in a healthy late-middle-aged population with a negative status for AD pathology. When initiating the study, [18F]THK5351 was a promising tau radiolabel. However, as detailed in the method section, [18F]THK5351 was later found to have an important unspecific binding to the neuroinflammation element (Chiotis et al., 2018). Although unintended, results with [18F]THK5351 will therefore reflect a combined burden of tau and neuroinflammation. This remains of interest as neuroinflammation is gaining increased attention as a potential mediator of cognitive impairment in aging (Kumar, 2018). With the present dataset, the authors predicted that protective factors (higher cognitive reserve and female sex) would be related to better performance in domain-specific cognition, whereas adverse factors (worse allostatic load, older age, APOE ε4 carrier status, higher Aβ, and tau/neuroinflammation burden) would be related to worse performance. We further anticipated that the influence of protective and adverse factors should be visible only for episodic memory and executive function (Hohman et al., 2017; McFall et al., 2019; Veldsman et al., 2020). As tau and APOE status were available only in a subsample of participants, analyses including these factors will be presented as exploratory.

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